5,542 research outputs found
Computer simulation of crystallization kinetics with non-Poisson distributed nuclei
The influence of non-uniform distribution of nuclei on crystallization
kinetics of amorphous materials is investigated. This case cannot be described
by the well-known Johnson-Mehl-Avrami (JMA) equation, which is only valid under
the assumption of a spatially homogeneous nucleation probability. The results
of computer simulations of crystallization kinetics with nuclei distributed
according to a cluster and a hardcore distribution are compared with JMA
kinetics. The effects of the different distributions on the so-called Avrami
exponent are shown. Furthermore, we calculate the small-angle scattering
curves of the simulated structures which can be used to distinguish
experimentally between the three nucleation models under consideration.Comment: 14 pages including 7 postscript figures, uses epsf.sty and
ioplppt.st
Multidimensional Quasi-Monte Carlo Malliavin Greeks
We investigate the use of Malliavin calculus in order to calculate the Greeks
of multidimensional complex path-dependent options by simulation. For this
purpose, we extend the formulas employed by Montero and Kohatsu-Higa to the
multidimensional case. The multidimensional setting shows the convenience of
the Malliavin Calculus approach over different techniques that have been
previously proposed. Indeed, these techniques may be computationally expensive
and do not provide flexibility for variance reduction. In contrast, the
Malliavin approach exhibits a higher flexibility by providing a class of
functions that return the same expected value (the Greek) with different
accuracies. This versatility for variance reduction is not possible without the
use of the generalized integral by part formula of Malliavin Calculus. In the
multidimensional context, we find convenient formulas that permit to improve
the localization technique, introduced in Fourni\'e et al and reduce both the
computational cost and the variance. Moreover, we show that the parameters
employed for variance reduction can be obtained \textit{on the flight} in the
simulation. We illustrate the efficiency of the proposed procedures, coupled
with the enhanced version of Quasi-Monte Carlo simulations as discussed in
Sabino, for the numerical estimation of the Deltas of call, digital Asian-style
and Exotic basket options with a fixed and a floating strike price in a
multidimensional Black-Scholes market.Comment: 22 pages, 6 figure
Hyperparameter Importance Across Datasets
With the advent of automated machine learning, automated hyperparameter
optimization methods are by now routinely used in data mining. However, this
progress is not yet matched by equal progress on automatic analyses that yield
information beyond performance-optimizing hyperparameter settings. In this
work, we aim to answer the following two questions: Given an algorithm, what
are generally its most important hyperparameters, and what are typically good
values for these? We present methodology and a framework to answer these
questions based on meta-learning across many datasets. We apply this
methodology using the experimental meta-data available on OpenML to determine
the most important hyperparameters of support vector machines, random forests
and Adaboost, and to infer priors for all their hyperparameters. The results,
obtained fully automatically, provide a quantitative basis to focus efforts in
both manual algorithm design and in automated hyperparameter optimization. The
conducted experiments confirm that the hyperparameters selected by the proposed
method are indeed the most important ones and that the obtained priors also
lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s).
Publication rights licensed to ACM. This is the author's version of the work.
It is posted here for your personal use, not for redistribution. The
definitive Version of Record was published in Proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery & Data Minin
The use of plasma-based deposition with ion implantation technology to produce superhard molybdenum-based coatings in a mixed (C₂H₂+N₂) atmosphere
The influence of the pressure of a mixed gaseous atmosphere (80%C₂H₂+20%N₂) and the supply of a high-voltage negative potential in a pulsed form on the elemental and phase composition, structure and physico-mechanical characteristics of the vacuum-arc molybdenum-based coating
Quantum dynamics in canonical and micro-canonical ensembles. Part I. Anderson localization of electrons
The new numerical approach for consideration of quantum dynamics and
calculations of the average values of quantum operators and time correlation
functions in the Wigner representation of quantum statistical mechanics has
been developed. The time correlation functions have been presented in the form
of the integral of the Weyl's symbol of considered operators and the Fourier
transform of the product of matrix elements of the dynamic propagators. For the
last function the integral Wigner- Liouville's type equation has been derived.
The numerical procedure for solving this equation combining both molecular
dynamics and Monte Carlo methods has been developed. For electrons in
disordered systems of scatterers the numerical results have been obtained for
series of the average values of the quantum operators including position and
momentum dispersions, average energy, energy distribution function as well as
for the frequency dependencies of tensor of electron conductivity and
permittivity according to quantum Kubo formula. Zero or very small value of
static conductivity have been considered as the manifestation of Anderson
localization of electrons in 1D case. Independent evidence of Anderson
localization comes from the behaviour of the calculated time dependence of
position dispersion.Comment: 8 pages, 10 figure
Pricing and Hedging Asian Basket Options with Quasi-Monte Carlo Simulations
In this article we consider the problem of pricing and hedging
high-dimensional Asian basket options by Quasi-Monte Carlo simulation. We
assume a Black-Scholes market with time-dependent volatilities and show how to
compute the deltas by the aid of the Malliavin Calculus, extending the
procedure employed by Montero and Kohatsu-Higa (2003). Efficient
path-generation algorithms, such as Linear Transformation and Principal
Component Analysis, exhibit a high computational cost in a market with
time-dependent volatilities. We present a new and fast Cholesky algorithm for
block matrices that makes the Linear Transformation even more convenient.
Moreover, we propose a new-path generation technique based on a Kronecker
Product Approximation. This construction returns the same accuracy of the
Linear Transformation used for the computation of the deltas and the prices in
the case of correlated asset returns while requiring a lower computational
time. All these techniques can be easily employed for stochastic volatility
models based on the mixture of multi-dimensional dynamics introduced by Brigo
et al. (2004).Comment: 16 page
Study of the system in the mass range up to 1200 MeV
The reaction has been studied with GAMS-2000
spectrometer in the secondary 38 GeV/c -beam of the IHEP U-70
accelerator. Partial wave analysis of the reaction has been performed in the
mass range up to 1200 MeV. The -meson is seen as a sharp
peak in S-wave. The -dependence of production cross section has
been studied. Dominant production of the at a small transfer
momentum confirms the hypothesis of Achasov and Shestakov about significant
contribution of the exchange () in the mechanism
of meson production in -channel of the reaction.Comment: 4 pages, 3 figures, talk given at HADRON'9
Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation
A central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from in vivo clinical measurements in a timeframe that is suitable for use within a clinic. Understanding these properties can provide insight into heart function or dysfunction and help to inform personalized medicine. However, finding a solution to the differential equations which mathematically describe the kinematics and dynamics of the myocardium through numerical integration can be computationally expensive. To circumvent this issue, we use the concept of emulation to infer the myocardial properties of a healthy volunteer in a viable clinical timeframe by using in vivo magnetic resonance image data. Emulation methods avoid computationally expensive simulations from the left ventricular model by replacing the biomechanical model, which is defined in terms of explicit partial differential equations, with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving computational efficiency at the clinic. We compare and contrast two emulation strategies: emulation of the computational model outputs and emulation of the loss between the observed patient data and the computational model outputs. These strategies are tested with two interpolation methods, as well as two loss functions. The best combination of methods is found by comparing the accuracy of parameter inference on simulated data for each combination. This combination, using the output emulation method, with local Gaussian process interpolation and the Euclidean loss function, provides accurate parameter inference in both simulated and clinical data, with a reduction in the computational cost of about three orders of magnitude compared with numerical integration of the differential equations by using finite element discretization techniques
Recurrent mutations of BRCA1, BRCA2 and PALB2 in the population of breast and ovarian cancer patients in Southern Poland
Background Mutations in the BRCA1, BRCA2 and PALB2 genes are well-established risk factors for the development of breast and/or ovarian cancer. The frequency and spectrum of mutations in these genes has not yet been examined in the population of Southern Poland. Methods We examined the entire coding sequences of the BRCA1 and BRCA2 genes and genotyped a recurrent mutation of the PALB2 gene (c.509_510delGA) in 121 women with familial and/or early-onset breast or ovarian cancer from Southern Poland. Results A BRCA1 mutation was identified in 11 of 121 patients (9.1 %) and a BRCA2 mutation was identified in 10 of 121 patients (8.3 %). Two founder mutations of BRCA1 accounted for 91 % of all BRCA1 mutation carriers (c.5266dupC was identified in six patients and c.181 T > G was identified in four patients). Three of the seven different BRCA2 mutations were detected in two patients each (c.9371A > T, c.9403delC and c.1310_1313delAAGA). Three mutations have not been previously reported in the Polish population (BRCA1 c.3531delT, BRCA2 c.1310_1313delAAGA and BRCA2 c.9027delT). The recurrent PALB2 mutation c.509_510delGA was identified in two patients (1.7 %). Conclusions The standard panel of BRCA1 founder mutations is sufficiently sensitive for the identification of BRCA1 mutation carriers in Southern Poland. The BRCA2 mutations c.9371A > T and c.9403delC as well as the PALB2 mutation c.509_510delGA should be included in the testing panel for this population
Parental bonding and identity style as correlates of self-esteem among adult adoptees and nonadoptees
Adult adoptees (n equals 100) and non-adoptees (n equals 100) were compared with regard to selfesteem, identity processing style, and parental bonding. While some differences were found with regard to self-esteem, maternal care, and maternal overprotection, these differences were
qualified by reunion status such that only reunited adoptees differed significantly from nonadoptees.
Moreover, hierarchical regression analyses indicated that parental bonding and identity processing style were more important than adoptive status per se in predicting self esteem. Implications for practitioners who work with adoptees are discussed
- …